Two-dimensional DOA Research Articles

Two-Dimensional DOA Estimation for Monostatic MIMO Radar with Electromagnetic Vector Received Sensors

We study two-dimensional direction of arrival (2D-DOA) estimation problem of monostatic MIMO radar with the receiving array which consists of electromagnetic vector sensors (EMVSs). The proposed angle estimation algorithm can be applied to the arbitrary and unknown array configuration, which can be summarized as follows. Firstly, EMVSs in the receiver of a monostatic MIMO radar are used to measure all six electromagnetic-field components of an incident wavefield. The vector sensor array with the six unknown electromagnetic-field components is divided into six spatially identical subarrays. Secondly, ESPRIT is utilized to estimate the rotational invariant factors (RIFs). Parts of the RIFs are picked up to restore the source’s electromagnetic-field vector. Last, a vector cross product operation is performed between electric field and magnetic field to obtain the Pointing vector, which can offer the 2D-DOA estimation. Prior knowledge of array elements’ positions and angle searching procedure are not necessary for the proposed 2D-DOA estimation method. Simulation results prove the validity of the proposed method.

Two-Dimensional DoA Estimation for Multipath Propagation Characterization Using the Array Response of PN-Sequences

Multipath propagation and power arrival profiles in three-dimensional (3-D) space determine the performance of the full-dimensional MIMO (FD-MIMO) systems. Field channel measurements are crucial in characterizing wireless channel properties. Nevertheless, in spatial channel measurements, estimating the direction-of-arrivals (DoAs) of multipath components (MPCs) is a challenging issue, because of the large number of propagation paths and the correlation among the multipath signals. The number of incidence angles and estimation precision in traditional methods is limited by the sensor array size and signal correlation. In this paper, we propose a scheme for measuring and estimating the 2-D DoAs of propagation paths called multipath angular estimation using the array response of PN-sequences (MAPS). By using a receiving planar antenna array (PAA), MAPS first extracts the complex path array response vector (PARV) for each propagation path and then estimates the DoAs of the paths individually and independently. The subspace-decomposition theory for MAPS is proved and extensive simulations are conducted to compare MAPS with other algorithms. Furthermore, a channel sounder using two PAAs and the probing signal of 2.6 GHz carrier modulated by PN-sequences has been developed. The simulation and field tests show that MAPS can estimate arbitrary number of resolved MPCs in a channel snapshot and effectively suppress the multipath interference.

Study of High Resolution DOA Estimation Based on Vector-Hydrophone

Vector-hydrophone can simultaneously measure acoustic pressure and orthogonal components of the particle velocity. The 180o ambiguity in DOA estimation can be eliminated using information obtained by vector hydrophone array. Multiple signal classification algorithm is a method that takes the eigen-decomposition of data co-variance matrix to obtain the estimation of signal spatial spectrum. The two-dimensional DOA of acoustic sources is estimated based on multiple signal classification algorithm using the vector-hydrophone uniform linear array. Simulation results show that better DOA resolution performance can be obtained from vector hydrophones. Furthermore, the paper takes the de-correlation of correlated sources using spatial smoothness technology to obtain perfect performance of two-dimensional DOA estimation.

Two-dimensional angle estimation for monostatic MIMO arbitrary array with velocity receive sensors and unknown locations

In this paper, the problem of two-dimensional angle estimation for monostatic multi-input multi-output (MIMO) array is studied, and an algorithm based on the usage of velocity receive sensors is proposed. The algorithm applies the estimation method of signal parameters via rotational invariance technique (ESPRIT) algorithm to obtain automatically paired two-dimensional angle estimation. By utilizing the relationship within the outputs of velocity sensors, the rotational invariance property of ESPRIT does not depend on the array geometry any more. Hence, the proposed algorithm can provide two-dimensional DOA estimation for the MIMO array without the knowledge of sensor locations in the array. The algorithm requires no peak searches, so it has low complexity. Furthermore, it has better angle estimation performance than propagator method using the same sensor configuration. Error analysis and Cramér–Rao bound (CRB) of angle estimation in MIMO radar are derived. Simulation results verify the usefulness of the algorithm.

Two-dimensional DOA Estimation with L-shaped Array Based on a Jointly Sparse Representation

Two-dimensional DOA Estimation with L-shaped Array Based on a Jointly Sparse Representation

Reply to “Comments on `L-Shape Two-Dimensional Arrival Angle Estimation With Propagator Method'”

In this reply to the paper by Shu, Liu and Lu (see ibid., p.1502-3, 2008), a novel method with low-complexity, but showing neither estimation failure nor the pair-matching problems, is described for the two-dimensional DOA estimation. The authors in the previous papers (Kikuchi et al., 2006; Gu et al, 2008)also proposed two methods different from that in (Van der Veen et a., 1992) to achieve the automatic pair matching by employing the L-shaped array configuration. The method in this reply is also different from the ones in (Kikuchi et al., 2006; Gu et al, 2008). In this reply, an independently developed algorithm is presented by using the unitary root MUSIC and unitary MUSIC and changing the L-shape to a cross-shaped array configuration.

ダイポールアレーアンテナを用いた波形再生による単一パルス電磁波の2次元到来方向推定

ダイポールアレーアンテナを用いた波形再生による単一パルス電磁波の2次元到来方向推定

MUSIC and maximum likelihood techniques on two-dimensional DOA estimation with uniform circular array

The authors describe the application of multiple signal classification (MUSIC) and maximum likelihood (ML) techniques to the joint azimuthal and elevational directions-of-arrival (AEDOA) estimation with a uniform circular array. Both deterministic and random source signal models are considered. The asymptotic statistical properties of MUSIC and ML estimation error vectors for AEDOA parameters are investigated. In particular, explicit analytical expressions are derived for asymptotic MUSIC and ML covariance matrices as well as Cramer-Rao lower bounds (CRLBs). These analytical formulae are employed in the theoretical performance study. Computer simulation results are presented to validate theoretical predictions and compare the performance of MUSIC and ML methods. It is shown that the performance of unconditional ML is superior to that of deterministic ML, which is, in turn, better than that of MUSIC.